{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\cx\\Anaconda\\envs\\CX\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
            "  from .autonotebook import tqdm as notebook_tqdm\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "pygame 2.6.1 (SDL 2.28.4, Python 3.13.2)\n",
            "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import mesa\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import clear_output\n",
        "%matplotlib inline\n",
        "import sys,os,inspect\n",
        "if hasattr(sys.modules[__name__], '__file__'):\n",
        "    _file_name = __file__\n",
        "else:\n",
        "    _file_name = inspect.getfile(inspect.currentframe())\n",
        "CURRENT_FILE_PATH = os.path.dirname(_file_name)\n",
        "sys.path.append(os.getcwd()+\"/../src/\")\n",
        "import Animal\n",
        "import Brain\n",
        "import Game"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "class parameter():\n",
        "  world_width = 25\n",
        "  world_height = 25\n",
        "\n",
        "  grass_born_size = 1\n",
        "  grass_grow_speed = 0.01\n",
        "  grass_max_size = 10\n",
        "\n",
        "  sheep_born_size = 2\n",
        "  sheep_max_size = 10000\n",
        "  sheep_base_energy_decay = 0.005\n",
        "  sheep_move_energy_cost = 0.02\n",
        "  sheep_eat_speed = 2\n",
        "  sheep_observation_radius = 6\n",
        "  sheep_max_speed = 2\n",
        "\n",
        "  wolf_born_size = 10\n",
        "  wolf_max_size = 10000\n",
        "  wolf_base_energy_decay = 0\n",
        "  wolf_move_energy_cost = 0\n",
        "  wolf_eat_speed = 0.5\n",
        "  wolf_observation_radius = 4\n",
        "  wolf_max_speed = 3\n",
        "\n",
        "  n_grass = 200\n",
        "  n_sheep = 25\n",
        "  n_wolf = 10\n",
        "\n",
        "  QLearning_random = 0.3\n",
        "\n",
        "parameter_dict = parameter()\n",
        "def printpara():\n",
        "  for name, value in inspect.getmembers(parameter_dict):\n",
        "    if not name.startswith('__'):\n",
        "      print(f\"{name}: {value}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## World"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "class World(mesa.Model):\n",
        "  def __init__(self,parameter,seed=None):\n",
        "    super().__init__(seed=seed)  \n",
        "    self.parameter = parameter\n",
        "    self.width = self.parameter.world_width\n",
        "    self.height = self.parameter.world_height\n",
        "    self.grid = mesa.space.MultiGrid(self.parameter.world_width,self.parameter.world_height,torus=False)\n",
        "    self.history = {}\n",
        "    self.steps = 0\n",
        "    self.non_overlapping_grid = []\n",
        "    self.training_brain_list = []\n",
        "    self.cxid = 0\n",
        "  def add_agent_automatic(self):\n",
        "    self.add_agent(Animal.Animal(self.cxid,self,self.training_brain_list[0],'Sheep',prey=['Grass'],predator=['Wolf'],cowoker=['Sheep'],\n",
        "                                energy=self.parameter.sheep_born_size,\n",
        "                                max_energy=self.parameter.sheep_max_size,\n",
        "                                eat_speed=self.parameter.sheep_eat_speed,\n",
        "                                observation_radius=self.parameter.sheep_observation_radius,\n",
        "                                base_energy_decay=self.parameter.sheep_base_energy_decay,\n",
        "                                move_energy_cost=self.parameter.sheep_move_energy_cost))\n",
        "    self.cxid += 1\n",
        "  def add_agent(self,agent):\n",
        "    x = random.randrange(self.width)\n",
        "    y = random.randrange(self.height)\n",
        "    self.grid.place_agent(agent,(x,y))\n",
        "  def add_non_overlapping_agent(self,agent):\n",
        "    while 1:\n",
        "      x = random.randrange(self.width)\n",
        "      y = random.randrange(self.height)\n",
        "      if (x,y) not in self.non_overlapping_grid:\n",
        "        self.non_overlapping_grid.append((x,y))\n",
        "        break\n",
        "    self.grid.place_agent(agent,(x,y))\n",
        "  def remove_agent(self,agent):\n",
        "    self.agents.remove(agent)\n",
        "  def step(self):\n",
        "    self.agents.shuffle_do(\"step\")\n",
        "    for i in self.training_brain_list:\n",
        "      loss = i.replay(batch_size=32)\n",
        "      if (self.steps) % 10 == 0:\n",
        "        i.update_target_network()\n",
        "      if (self.steps) % 100 == 0:\n",
        "        i.save_weight(f'brain_new/weight_{self.steps}.pth')\n",
        "  def dynamic_visualization(self,cx_steps=100,interval=0.1,plot_parameter={}):\n",
        "    figure = plt.figure(figsize=(8,6))\n",
        "    plt.rcParams['font.family'] = 'Segoe UI Emoji'\n",
        "    for step in range(cx_steps):\n",
        "      self.step()\n",
        "      plt.clf()\n",
        "      ax = plt.gca()\n",
        "      tmp = {}\n",
        "      # statics on animals\n",
        "      for agent in self.agents:\n",
        "        if agent.agent_class not in tmp:\n",
        "          tmp[agent.agent_class] = {}\n",
        "        tmp[agent.agent_class][agent.unique_id] = {'position':agent.pos,'energy':agent.energy,'action':agent.action,'speed':agent.speed}\n",
        "      self.history[self.steps] = tmp\n",
        "      if 'Sheep' not in tmp:\n",
        "        n = 0\n",
        "      else:\n",
        "        n = len(tmp['Sheep'])\n",
        "      for j in range(parameter_dict.n_sheep-n):\n",
        "        self.add_agent_automatic()\n",
        "      for agent_class in tmp:\n",
        "        for id in tmp[agent_class]:\n",
        "          plt.text(tmp[agent_class][id]['position'][0],tmp[agent_class][id]['position'][1],plot_parameter[agent_class]['text'],ha='center',va='center',fontsize=tmp[agent_class][id]['energy'])\n",
        "          if np.sum(abs(np.array(tmp[agent_class][id]['speed'])))!=0:\n",
        "            plt.arrow(tmp[agent_class][id]['position'][0],tmp[agent_class][id]['position'][1],tmp[agent_class][id]['action'][0]*0.4,tmp[agent_class][id]['action'][1]*0.4,width=0.1)\n",
        "      ax.set_xlim(0,self.width)\n",
        "      ax.set_ylim(0,self.height)\n",
        "      plt.title(f\"Ecosystem Simulation - Step {step}\")\n",
        "      plt.xlabel(\"X Position\")\n",
        "      plt.ylabel(\"Y Position\")\n",
        "      plt.pause(interval)\n",
        "      clear_output(wait=True)\n",
        "    plt.ioff()\n",
        "    plt.show()\n",
        "    return self.history\n",
        "  \n",
        "  def dynamic_visualization_pygame(self,cx_steps=100,interval=0.1,plot_parameter={}):\n",
        "    for step in range(cx_steps):\n",
        "      self.step()\n",
        "      tmp = {}\n",
        "      # statics on animals\n",
        "      for agent in self.agents:\n",
        "        if agent.agent_class not in tmp:\n",
        "          tmp[agent.agent_class] = {}\n",
        "        tmp[agent.agent_class][agent.unique_id] = {'position':agent.pos,'energy':agent.energy,'action':agent.action,'speed':agent.speed}\n",
        "      self.history[self.steps] = tmp\n",
        "      if 'Sheep' not in tmp:\n",
        "        n = 0\n",
        "      else:\n",
        "        n = len(tmp['Sheep'])\n",
        "      for j in range(parameter_dict.n_sheep-n):\n",
        "        self.add_agent_automatic()\n",
        "      self.game.checkQuit()\n",
        "\n",
        "      self.game.update(agentsStake=tmp)\n",
        "      self.game.render()\n",
        "    self.game.quit()\n",
        "\n",
        "    return self.history"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Simulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "seed = 0\n",
        "tmp = (parameter_dict.sheep_observation_radius*2+1)\n",
        "field = (tmp,tmp,3)\n",
        "sheep_brain = Brain.Brain_DDPG(observation_radius=parameter_dict.sheep_observation_radius,observation_channel=3,epsilon=parameter_dict.QLearning_random,max_velocity=parameter_dict.sheep_max_speed,mode=1)\n",
        "wolf_brain = Brain.Brain_Random()\n",
        "world = World(parameter_dict)\n",
        "world.training_brain_list = [sheep_brain]\n",
        "c = 0\n",
        "for _ in range(parameter_dict.n_grass):\n",
        "  world.add_non_overlapping_agent(Animal.Producer(c,world,'Grass',\n",
        "                                  energy=parameter_dict.grass_born_size,\n",
        "                                  max_energy=parameter_dict.grass_max_size,\n",
        "                                  grow_speed=parameter_dict.grass_grow_speed))\n",
        "  c += 1\n",
        "for _ in range(parameter_dict.n_sheep):\n",
        "  world.add_agent(Animal.Animal(c,world,sheep_brain,'Sheep',prey=['Grass'],predator=['Wolf'],cowoker=['Sheep'],\n",
        "                                energy=parameter_dict.sheep_born_size,\n",
        "                                max_energy=parameter_dict.sheep_max_size,\n",
        "                                eat_speed=parameter_dict.sheep_eat_speed,\n",
        "                                observation_radius=parameter_dict.sheep_observation_radius,\n",
        "                                base_energy_decay=parameter_dict.sheep_base_energy_decay,\n",
        "                                move_energy_cost=parameter_dict.sheep_move_energy_cost))\n",
        "  c += 1\n",
        "for _ in range(parameter_dict.n_wolf):\n",
        "  world.add_agent(Animal.Animal_Test(c,world,'Wolf',prey=['Sheep'],predator=[],cowoker=[],\n",
        "                                     energy=parameter.wolf_born_size,\n",
        "                                     eat_speed=parameter.wolf_eat_speed))\n",
        "  c += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x:0,y:0,w:80,h:79\n",
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\xchen\\Documents\\workspace\\AI\\ai_world\\world_simulation\\offline/../src\\Brain.py:173: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\utils\\tensor_new.cpp:257.)\n",
            "  actions = torch.FloatTensor([t[1] for t in batch])\n"
          ]
        },
        {
          "name": "stdout",
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            "[1967]:耗费90毫秒\n",
            "[1968]:耗费79毫秒\n",
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            "[1989]:耗费89毫秒\n",
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            "[1991]:耗费92毫秒\n",
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            "[2009]:耗费90毫秒\n",
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            "[2011]:耗费98毫秒\n",
            "[2012]:耗费85毫秒\n",
            "[2013]:耗费83毫秒\n",
            "[2014]:耗费83毫秒\n",
            "[2015]:耗费93毫秒\n",
            "[2016]:耗费78毫秒\n",
            "[2017]:耗费96毫秒\n",
            "[2018]:耗费87毫秒\n",
            "[2019]:耗费80毫秒\n",
            "[2020]:耗费99毫秒\n",
            "[2021]:耗费86毫秒\n",
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            "[2099]:耗费100毫秒\n",
            "[2100]:耗费98毫秒\n",
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            "[2397]:耗费94毫秒\n",
            "[2398]:耗费97毫秒\n",
            "[2399]:耗费87毫秒\n",
            "[2400]:耗费87毫秒\n",
            "[2401]:耗费83毫秒\n",
            "[2402]:耗费83毫秒\n",
            "[2403]:耗费87毫秒\n",
            "[2404]:耗费84毫秒\n",
            "[2405]:耗费97毫秒\n",
            "[2406]:耗费100毫秒\n",
            "[2407]:耗费109毫秒\n",
            "[2408]:耗费114毫秒\n"
          ]
        },
        {
          "ename": "SystemExit",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "An exception has occurred, use %tb to see the full traceback.\n",
            "\u001b[31mSystemExit\u001b[39m\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "d:\\cx\\Anaconda\\envs\\CX\\Lib\\site-packages\\IPython\\core\\interactiveshell.py:3675: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
            "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
          ]
        }
      ],
      "source": [
        "world.game=Game.GameRender()\n",
        "world.game.FPS=100\n",
        "history = world.dynamic_visualization_pygame(cx_steps=10000,interval=0.1,plot_parameter={'Grass':{'text':'🌿'},'Sheep':{'text':'🐑'},'Wolf':{'text':'🐺'}})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.13.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
